How We’re Making Publicly Available Data More User Friendly
We live in an age of data. Big companies like Google and Amazon are using data to refine their products and target advertising. Researchers and planners are using data to come up with ways of improving where we live.
But is data something that non-experts can use, for their jobs or everyday use?
The Data Is Out There
There is a ton of free, high-quality, public data released by government agencies, which can be used by anyone. This data could help a student with a project, help a small organization find areas of need, or help a city planner get a quick overview of their area.
There is an abundance of data available for public download. The Census Bureau alone has a huge amount of valuable information, going down to the neighborhood-level. There’s the basic stuff: race, age, income, things like that. And that’s incredibly useful. But maybe you’re interested in something more specific: language spoken at home, people with GEDs, people without health insurance.
The Data Isn’t Easy To Use
And there’s a lot more data available than the Census data. The Bureau of Labor Statistics has unemployment and industry data. The Centers for Disease Control and Prevention (CDC) has an incredible amount of health data. Want high school graduation rates? The National Center for Education Statistics has that. Broadband availability? Federal Communications Commission (FCC). Flood zones? Federal Emergency Management Agency (FEMA).
And that’s not even considering some of the affordable proprietary data available for purchase, covering topics like home sales, vacancy, and school performance.
It’s good data, and it can be used to make decisions on how to solve real problems.
But there’s a problem: It’s not always easy to find or to use. The data you want may come from three different sources. It might not be clear which indicators are right for you. It might not be clear what data is valid. It might not be clear how to download it. And even if you get that far, you may have seven different files from four websites, all with differently formatted data that you need to clean.
Data comes in many formats; there’s long format data, there’s wide format data, there are XLS files, there are CSV files. American Community Survey (ACS) Census Bureau summary files have their column headers in a different file. There’s a lot of great data from Longitudinal Employer – Household Dynamics, but accessing it requires downloading and processing almost a terabyte of data, far beyond the capacity of an inexperienced data user.
Oh, and good luck getting it on a map, unless you’ve taken GIS classes.
There are enough obstacles that people who could really benefit don’t take advantage of what’s available.
Make Data Easier to Access
And that’s how PolicyMap came into being. It was started by a community development financial institution (Reinvestment Fund, in Philadelphia), almost ten years ago. They had the in-house expertise to obtain and work with the data, but saw how many other similar organizations didn’t, and how much access to data would help them.
PolicyMap is two things: It’s an online data warehouse and a mapping platform.
Building a Data Warehouse
We have a whole team dedicated to data. We’re keeping track of data updates, looking for new data, validating data, deciding what data is useful and what data is isn’t, and writing clear labels describing what the data is.
On our website, users can easily find the data they want by either navigating our thematic menus (Demographics, Health, Education, Economy, etc.), or by searching for specific terms. And just like that, they have the data. Data is downloadable in a consistent, usable format.
We’ve gotten so adept at keeping our data simple to use and up-to-date that some larger entities subscribe to our API, just so they can get all the data they need from one place.
Most of PolicyMap’s users want the data for a specific purpose: To put it on a map. And so, as our name implies, that’s the first thing you get when you open an indicator on our site. Maps can show distinct patterns, such as where poverty exists, where high-paying jobs are, or where diseases are prevalent.
It also makes it easy to get exact values for a specific place; type in an address, and immediately know the median income for your neighborhood.
Desktop GIS applications are amazingly advanced in the analytical tools they offer, but are also notoriously difficult to use, even for trained professionals. PolicyMap offers an alternative when what you really need is to map the data. Of course, advanced users can use PolicyMap to download the data, and then run their more advanced analyses.
PolicyMap offers an analytical tool of its own: 3-Layer Maps. You can choose one, two, or three indicators, and find areas that match specific criteria. For example, if you wanted to find areas in your city where Supplemental Nutrition Assistance Program (SNAP) benefits are being underutilized, you might bring up data on poverty and SNAP recipients, and show areas where poverty is high, but SNAP use is low. Then use that information to fix a problem.
Make Data Available for Everyone
If data is publicly available, it’s free to access on PolicyMap, for anyone. That’s been one of our core principles from the beginning, even though charging for everything would be more lucrative. We do offer subscriptions, with data from private sources, and additional features, like 3-Layer Maps, reports, and the ability to load your own data. We also have relationships with organizations, businesses, and governments to make custom applications for their purposes.
But at the end of the day, what’s important is that data is available for everyone.
Leave your comment below, or reply to others.
Read more from the Meeting of the Minds Blog
Spotlighting innovations in urban sustainability and connected technology
For almost a year, our team has been working on a toolkit to help readers navigate the nuanced, complicated conversations that surround algorithms and the data that they consume. The project came about after a small workshop held in the city of San Francisco in February of 2018. The conversation around data science and transparency for laypeople brought us to the idea that a new resource was needed to bridge the gap between data scientists and non-data scientists.
A mid-sized city’s demonstration corridor for innovation in safety, sustainability, and multimodal mobility.
Has the future of mobility arrived yet? Of course, we haven’t reached our final destination, but there are reasons to feel good about our overall progress. A couple cities have made great strides toward the end goal of MaaS, and their successes should serve as examples to other urban areas and regions considering their own next steps.